APPLIED SCIENTIST JOB DESCRIPTION

An Applied Scientist translates statistical and machine learning research into measurable business outcomes. In cloud infrastructure and enterprise analytics contexts, this means owning the path from ambiguous problem framing to production-grade models influencing high-stakes capacity and investment decisions worth hundreds of millions in annual operational spend. Demand forecasting across 200-plus online services, or quantifying cross-channel marketing lift at web scale - demands rigor that neither pure research nor standard engineering provides. Science drives the work.

As the Applied Scientist, you translate formulated business problems into experimental designs, validated ML models, and deployed forecasting systems that directly shape engineering and executive decisions across the organization. You will work embedded within cross-functional v-teams spanning engineering, operations, finance, and product management, applying advanced statistical and machine learning methods to datasets and problem spaces that evolve faster than off-the-shelf solutions can address.

Career Impact: Deep ownership of demand forecasting models or causal inference systems in a cloud-scale environment builds the kind of quantitative credibility that opens senior scientist and principal researcher tracks within three to five years.

Business Impact: The models you deploy directly inform capacity investment decisions affecting hundreds of millions in infrastructure spend, when your forecasts are wrong, the organization over-provisions or faces outages.

Growth Opportunity: Working at the intersection of big data engineering and applied ML research exposes you to Spark-scale pipelines, causal inference methods, and executive-facing communication skills that compound into rare, dual-domain expertise.

• Design and deploy scalable ML and statistical models to measure business impact across cloud demand, capacity planning, or marketing investment decisions.
• Formulate ambiguous business problems into tractable research questions, defining technical scope and experimental approach before implementation begins.
• Develop forecasting pipelines that integrate multiple data sources, compensate for data limitations, and generate timely insights for senior decision-making systems.
• Collaborate with engineering, product, and finance stakeholders to translate business needs into measurable metrics and system-level requirements.
• Validate model outputs against known biases, statistical errors, and production benchmarks to ensure results are reliable and reproducible.
• Present findings, proposals, and technical plans to audiences ranging from data engineering teams to senior executives, adjusting depth and framing accordingly.
• Monitor deployed models continuously and drive iterative improvements based on performance degradation, new data signals, or shifting business conditions.
• Lead or contribute to proposal development, peer-reviewed publication efforts, or internal knowledge-sharing to advance team-level scientific capability.

• Bachelor's degree in Computer Science, Statistics, Mathematics, Operations Research, or a related quantitative field, or equivalent work experience.
• 4 or more years of applied research or industry experience solving real-world problems with statistical modeling, ML algorithms, or data mining methods.
• Demonstrated ability to translate ambiguous business or engineering problems into well-defined experimental designs and technical solutions.
• Proficiency in statistical modeling techniques including regression, time series analysis, and experimental design for causal or predictive inference.
• Hands-on experience building and deploying machine learning models against large-scale datasets in production or near-production environments.
• Strong programming competency in at least one scripting or scientific computing language, with experience processing and transforming high-volume structured and unstructured data.
• Experience presenting analytical findings and technical recommendations to cross-functional audiences, including non-technical business or executive stakeholders.

• PhD in Computer Science, Machine Learning, Statistics, or a related quantitative discipline, with published or peer-reviewed research contributions.
• Experience with distributed data processing frameworks and cloud-scale infrastructure in contexts where data volume exceeds single-machine processing capacity.
• Familiarity with causal inference methods, such as difference-in-differences, instrumental variables, or synthetic control - applied to marketing measurement or operational planning.
• Prior exposure to supply chain, demand planning, or capacity forecasting within a large-scale technology or cloud services organization.

• Forecast accuracy rate for demand or capacity models, measured against actuals at defined time horizons.
• Mean time from problem formulation to validated model deployment, reflecting development velocity and scope management.
• Proportion of deployed models still performing within acceptable error bounds after 90 days, indicating production stability.
• Number of business decisions directly supported by model outputs per quarter, traceable to work product.
• Reduction in forecast error variance across successive model iterations, demonstrating continuous improvement cadence.
• Typical tools: statistical computing environments (commonly Python with scikit-learn or R); distributed processing (commonly Spark or Hadoop); version control and containerization (commonly Git, Docker).

• Base Salary Range: $145,000 to $195,000 annually, depending on seniority and location
• Bonus: Performance-based annual bonus, typically 10% to 20% of base salary
• Equity: RSU grants common at tech employers; typical vesting over four years
• Health Benefits: Comprehensive medical, dental, and vision coverage for employee and dependents
• PTO: 15 to 25 days annually, plus company holidays
• Common Perks: Conference and publication sponsorship, continuing education budget, home office stipend

Figures are estimates based on general US market benchmarks and may be outdated. Adjust based on location, company size, and seniority level.

Work authorization in the United States is required; candidates must be eligible to work without sponsorship, or sponsorship availability must be confirmed prior to offer. Employment in this role is contingent on successful completion of a background check, which may include criminal history and, for roles with cloud infrastructure access, periodic re-screening as required by government or customer contracts. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, veteran status, age, sexual orientation, gender identity, or any other characteristic protected under applicable federal, state, and local law. Reasonable accommodations are available throughout the hiring process upon request.

The Applied Scientist leads the design and deployment of scalable ML and statistical models on a big data platform to measure the business impact of cross-channel marketing investments. Working closely with business units and engineering teams, this role builds the data-driven decision-making systems that connect marketing performance to measurable outcomes.

Key Responsibilities

• Design and build scalable solutions using statistical and ML models to measure business impact of cross-channel marketing investments.
• Work closely with business units and engineering teams to frame marketing measurement problems and formulate technical solutions.
• Develop a library of measurement, decision-making, and machine learning algorithms to enable data-driven decision-making.
• Lead the scoping, design, development, and deployment of software solutions on a big data platform to generate insights for decision-making systems.
• Innovate and invent ways to overcome technical limitations and enable new forms of analyses to drive key business decisions.

Required Qualifications

• Master's degree in Computer Science, Mathematics, Machine Learning, or a related quantitative field, or PhD with 2+ years of applied research experience.
• 4+ years of applied research experience building and deploying ML models for business applications.
• Advanced proficiency in statistical modeling, experimental design, causal inference, and machine learning algorithms.
• Hands-on experience building ML models with web-scale datasets and deploying them to production environments.
• Experience using object-oriented programming languages such as Java, C++, or Python.
• Knowledge of professional software engineering practices across the software development life cycle.
• Experience leading a small team of applied researchers or scientists to complete high-impact projects.

Must have publications or presentations in top Machine Learning, Deep Learning, or Data Mining journals or conferences.

Embedded within the Healthcare Machine Learning organization, the Senior Applied Scientist owns the development and deployment of NLP models tailored to healthcare use cases spanning providers, payers, patients, pharma, and regulators. Working closely with clinical and technical stakeholders, this role serves as the domain expert whose work connects advanced language modeling to measurable improvements in healthcare outcomes.

Core Functions

• Identify how NLP can add value to healthcare stakeholders, including providers, payers, patients, pharma, and regulators.
• Serve as domain expert in Natural Language Processing within Healthcare Machine Learning.
• Develop deploy-ready ML models tailored to healthcare use cases.
• Understand client requirements in depth and act as the main point of contact for product feature enquiries.
• Stay ahead of new developments in NLP and Healthcare ML by reviewing published literature.
• Present proposals, plans, and technical material to diverse audiences ranging from clinicians to board members.

Qualifications & Experience

• PhD or MS in NLP, Computational Linguistics, Computer Science, Machine Learning, AI, or a related field with strong grasp of ML methods for structured and unstructured data.
• 2+ years of post-PhD or 4+ years of post-MS work experience in applied NLP.
• Depth in at least one NLP domain such as Text Classification, Information Extraction, Language Modeling, Text Summarization, or Machine Translation.
• Understanding of healthcare IT infrastructure including DICOM, HL7, and FHIR.
• Experience with ontologies such as SNOMED-CT, ICD-10, RxNorm, and MeSH.
• Awareness of modern ML literature, including Transformers, graph ML, and multimodal models.
• Fluency in Python and at least one NLP library such as spaCy, HuggingFace, or AllenNLP, along with frameworks including PyTorch and TensorFlow.
• Working comfort with software development tools, including Git, Docker, and bash scripting, as well as Spark and SQL.

Reporting to the Interaction and Analytics Lab leadership, the Applied Scientist shapes research initiatives and client-facing analytics projects spanning human-machine collaboration, explainable ML, and computer vision. Partnering with PARC technical and business development teams, this role translates innovative research into market-ready applications that generate new business opportunities.

Primary Duties

• Work with PARC technical and business development teams to define new research areas and innovative business applications.
• Listen and probe clients to negotiate useful and feasible analytics projects.
• Explain the intuition behind technology options clearly and without jargon.
• Produce clear graphics and visualizations to communicate procedures and results.
• Lead and contribute to new customer engagement and business development efforts, including proposal development.
• Participate actively in generating peer-reviewed articles and inventions.

Skills & Qualifications

• MS or equivalent experience in Computer Science or Applied Statistics.
• Experience in hands-on software and algorithm development with a commitment to code quality.
• Proficiency in exploratory data analysis, data wrangling, and ETL processes, including handling missing and erroneous values.
• Ability to apply classification, regression, and clustering methods and select appropriate algorithms for various problem types.
• Knowledge of Deep Learning and applicable libraries such as Torch, Theano, or TensorFlow, as well as NLP experience as a plus.
• Experience with R libraries and Python libraries including Pandas, matplotlib, numpy, and scikit-learn.
• Strong presentation and written communication skills.
• Ability to construct repeatable, documented workflows.

Sitting at the intersection of statistical science and large-scale data engineering, the Senior Data & Applied Scientist delivers validated insights and continuous methodology improvements that inform product and engineering leadership decisions. Operating across data sourcing, experiment design, and cross-functional collaboration with PMs and data engineering, this role transforms complex business needs into implemented metrics that drive measurable outcomes.

Accountabilities

• Identify and integrate multiple data sources, developing methods to compensate for limitations and extend data applicability.
• Apply and develop tools and pipelines to collect, clean, and prepare massive volumes of data for analysis.
• Transform formulated problems into experiment implementation plans, applying appropriate methods and statistically validating results against biases.
• Interpret results and develop insights within business context, guiding risks and limitations.
• Present findings to product and engineering leadership and incorporate feedback into analyses.
• Validate, monitor, and drive continuous improvement to methods, proposing enhancements to data sources.
• Collaborate with PMs and data engineering to translate business needs into metrics and implement them.

Requirements

• BS or MS in Computer Science, Statistics, Operations Research, or a similar quantitative field, with PhD preferred.
• 4+ years of applying statistical modeling, ML, or data mining algorithms to real-world problems.
• Solid foundation in statistical modeling, machine learning algorithms, and experimental design.
• Deep understanding of big data systems including MapReduce technologies.
• Expert in one or more statistical software packages such as R or SAS, and one or more scripting languages such as Python, Scala, or SQL.
• Experience in writing Scala packages, contributing to open-source projects, and working with Spark for 2+ years.
• Experience with Kubernetes and container-based deployment environments.

A key member of the MCCP Cloud Services Demand Forecasting team, the Applied Scientist builds and implements complex forecasting models using time series, regression, and machine learning techniques to generate quality demand forecasts for Microsoft's global online services. Collaborating across Engineering, Operations, Sales, Capacity Planning, and Finance, this role ensures the cloud infrastructure supply chain is informed by rigorous, data-driven demand analysis.

Activities

• Implement complex forecasting models using time series, regression, and machine learning techniques to generate quality demand forecasts.
• Participate in cross-functional v-teams across Engineering, Operations, Sales, Capacity Planning, and Finance to improve the capacity supply chain.
• Collect and generate requirements for Data Engineering to access new data sources and for Data Science to build new forecasting models.
• Apply continuous improvement techniques to implement long-term solutions.
• Perform ad-hoc analysis on critical business issues related to cloud services demand and capacity.

Experience & Qualifications

• BS or MS in mathematics, statistics, economics, engineering, computer science, operations research, or a related technical field.
• 3+ years of experience in supply chain, demand planning, advanced analytics, or forecasting.
• Understanding and experience in forecasting science and predictive modeling.
• Knowledge of cloud infrastructure demand drivers and macro and micro trends affecting cloud industry capacity.
• Strong analytical problem-solving skills with ability to distill complex problems into solvable components and balance mathematical rigor with action orientation.
• Experience in data analytics and modeling using R or SQL, with willingness to learn either if not already familiar.
• Ability to communicate findings clearly to varied audiences using data visualizations, and to build relationships across disparate teams in a matrix environment.
• Ability to meet government and cloud security screening requirements, including passing a background check every two years.